Soil erosion and fine particle transport are two of the major challenges in food security and water quality for the growing global population. Information of the areas prone to erosion is needed to prevent the release of pollutants and the loss of nutrients. Sediment fingerprinting is becoming a widely used tool to tackle this problem, allowing to identify the sources of sediments in a catchment. Methods in fingerprinting techniques are still under discussion with tracer selection at the centre of the debate. We propose a novel method, termed as consensus ranking (CR), that combines the predictions of single-tracer models to identify non-conservative tracers. In this context, a numerical procedure to quantify the predictions of individual tracers is first delivered. The scoring function to rank the tracers is based on several random debates between tracers in which the tracer that prevents consensus is discarded. Based on these results, a conservativeness index (CI) is presented along with a clustering method to identify groups of similar tracers. To illustrate the CI and CR procedures, an artificial mixture created with real soil to independently test the method is analysed. The results demonstrate the capability of our method to identify non-conservative tracers beyond the capability of currently used selection methods. Further, a real sediment sample from a Mediterranean mountain catchment is evaluated to emphasise its utility in complex natural environments. To test the utility of our method, it was decided to include the conservative and consensus-enforcing tracers extracted by this new approach with two different unmixing models. Furthermore, CR and CI procedures are displayed together with the most widespread statistical tests and the within-a-polygon approach used for tracer selection in fingerprinting studies. The new proposed method will enable the research community to homogenise results for replicability as well as allowing comparisons among study areas.